Satellite navigation (satnav) systems such as the Global Positioning System (GPS) and similar global navigation satellite systems (GNSS) can be used for navigation of aerial craft, including both human crewed and/or remotely piloted and/or autonomous aircraft. Such craft include guided missiles, unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs). Satnav systems can inform a craft equipped with the appropriate receiver and processing apparatus of the craft's precise geographical location in space with respect to the Earth by delivering spatial coordinates which can then be used to navigate the craft, whether by onboard or remote pilots or by autonomous navigation systems. However, satnav systems exhibit less than total reliability, and moreover are occasionally unsuitable for certain navigation applications. Sensor readings from an inertial measurement unit (IMU) may also be used for navigation, but the position solutions from IMU sensor readings tend to drift over time and thus pose accuracy problems of their own. In view of the challenges presented by GPS- and IMU-based navigation, aerial vehicles may rely on alternate methods of navigation, including methods that estimate spatial position based on visual indicators. Spatial position may be estimated, for example, by matching features extracted from image frames representing aerial observations taken at different times (e.g., two consecutive frames), thus showing a relative change in spatial position between the two image acquisition times. Another way of estimating spatial position may be by comparing extracted features to features extracted from observations stored in a database, or to such features stored in a database, which images or features are “geo-registered” such that the spatial coordinates of the database images or features are known.
Vision-based or vision-aided navigation is difficult or impossible, however, where appropriate visual indicators are nonexistent or spurious. While an aerial vehicle traveling over well-charted urban terrain, for example, may be able to visually estimate spatial position by recognizing such landmark features as streets and buildings, an airborne craft traveling over water or desert may not be able to accurately estimate position from spurious visual features. These spurious visual features are referred to herein as “false features.” Examples of false features include wave crests on water or sand dune shapes on land. Such features are “false,” and do not aid aerial navigation, when they are ephemeral and cannot correspond to known positions. As such, false features can be likened to noise that should not be confused for useful signal. Images or image portions showing densely forested areas, grassland, tundra, glacial fields, and other types of terrain can similarly present false features that mislead or stymy efforts at vision-aided navigation, as can images or image portions that include the sky and clouds. A vision-aided positioning system can expend extensive computational resources in futile attempts to find false features and/or to match false feature configurations to known true feature topographies.